seshkumar @seshkumar

I'm a cardiologist. I've held dying hearts in my hands in the cath lab at 3 AM. And I need to tell you something that changes everything about how we prevent heart attacks. For decades, the entire field was built on one target: lower LDL cholesterol. Statins save lives — that's settled science. But too many of my patients did everything right — took their statins, hit their numbers, lived clean — and still ended up on my table with a ruptured artery. We were treating the smoke while the fire kept burning. The fire is inflammation. And the evidence is now overwhelming. The CANTOS trial proved it first — lowering inflammation independent of cholesterol reduced cardiac events. But the newer data is what keeps me up at night. AI-enhanced CT angiography can now detect inflamed arteries by measuring changes in the fat surrounding your coronary vessels — the perivascular fat attenuation index. Higher inflammation in the fat around even one artery independently predicts cardiac death. When multiple arteries show inflammation, the risk multiplies dramatically — even in patients whose cholesterol looks perfect. This isn't theoretical. This is measurable. Right now. On a scan you can get this month. Low-dose colchicine — a drug that's been around for centuries for gout — is now FDA-approved specifically for reducing cardiovascular events. It works by quieting the inflammatory cascade that destabilizes the plaque sitting in your arteries. A pill that costs pennies is saving lives the statins couldn't reach. And the next wave is already in Phase 3 trials. Ziltivekimab — an IL-6 inhibitor — targets the central inflammatory pathway driving atherosclerosis. Phase 2 data showed a 90% reduction in hsCRP. The ZEUS cardiovascular outcomes trial is enrolling now, with results expected late 2026 into 2027. If positive, anti-inflammatory therapy will become standard in managing heart disease alongside lipid-lowering. The era of inflammation-targeted cardiology is arriving. But it goes deeper than drugs. AI is now predicting heart failure and cardiac events 5+ years before symptoms — integrating CT imaging, electronic health records, and genetic data with accuracy that jumps far beyond traditional risk calculators. And polygenic risk scores — a simple genetic test that flags inherited cardiovascular risk — are now formally recognized as a risk-enhancing factor in the 2026 ACC/AHA guidelines. A single blood draw can reveal risk that's been silently building since birth. Decades before the first chest pain. Here's what this means for you right now — today: Ask your doctor for a high-sensitivity CRP test. It's cheap, routine, and measures the systemic inflammation that standard cholesterol panels completely miss. You can have perfect LDL and inflamed arteries that are quietly preparing to rupture. If your hsCRP is elevated, discuss low-dose colchicine with your physician. It's FDA-approved for exactly this. Push for a coronary CT angiography with AI plaque and inflammation analysis if you have risk factors. This isn't the stress test your parents got. This is 3D visualization of your actual arteries — with AI quantifying not just how much plaque you have, but what kind it is and whether the surrounding tissue is inflamed. Consider polygenic risk score testing — especially with a family history of early heart disease. It's now guideline-supported. And the foundation that never changes: move daily, eat real food, sleep 7-9 hours, manage stress, and know your numbers — ApoB, Lp(a), hsCRP, fasting insulin. I left Iran as a child with nothing. I rebuilt everything in a country that gave me the freedom to become a physician. I've spent twenty years watching patients get second chances. The ones who haunt me aren't the ones who died on my table. They're the ones who survived but never acted on what the science was telling them — years before the event that didn't have to happen. You can have perfect cholesterol and still have a heart attack. Inflammation plus genetics can drive plaque rupture in arteries that look "fine" on a standard panel. The myth that normal cholesterol means you're safe has cost more lives than I can count. We now have the tools to detect the fire — not just the smoke. AI to see it. Genetics to predict it. Drugs to quiet it. And the ancient basics — movement, real food, sleep, purpose — to prevent it from starting. Prevention is the new cure. And the science to make it real is no longer coming. It's here.

Anthropic pays $750,000+ a year for engineers who can build LLM architectures from scratch. Stanford taught the entire thing in 1 hour lecture & released it for free. Bookmark & watch this today before someone takes it down and read this article below

Swati Gupta @hrswatigupta

2 weeks ago

x.com/i/article/2060…

11 67 293 1.1M 1K

In recent times, I have added some great businesses to my watchlist through @soicfinance channel as part of their recent initiative. Thanks to @ishmohit1 for your wonderful in-depth details. Here is a glimpse of it - worth watching the actual videos - youtube.com/playlist?list=…

🚨 20 WEBSITES EVERY INDIAN TRADER SHOULD KNOW Most people trade blindly. Smart traders use tools. Here are 20 websites that can level up your game 👇 1- nseindia. com — official market data 2- bseindia. com — announcements & filings 3- screener. in — check fundamentals easily 4- tickertape. in — powerful stock insights 5- moneycontrol. com — news + portfolio 6- tradingview. com — pro-level charts 7- chartink. com — find breakout stocks 8- trendlyne. com — smart analytics 9- investing. com — global market view 10- zerodha. com — top trading platform 11- groww. in — beginner-friendly investing 12- upstox. com — fast & simple trading 13- smallcase. com — ready-made portfolios 14- valueresearchonline. com — MF analysis 15- motilaloswal. com — detailed reports 16- icicidirect. com — research + brokerage 17- indmoney. com — track all assets 18- varsity. zerodha. com — free trading course 19- elearnmarkets. com — learn from experts 20- sensibull. com — options strategies 💡 Save this. Use these. Grow faster in markets.

A scientist in Denmark figured out how to make Claude prepare his job applications. He open-sourced the whole thing. His name is Mads Lorentzen. He is a PhD geophysicist. He built it on top of Claude Code and released it under MIT license. Here is what it does. You fork the repo, fill in your background once, and it runs a five-step pipeline for every job you want to apply to. Step 1. It reads the job posting and scores how well you fit. Step 2. It drafts a tailored CV in LaTeX, picking only the experience that matches. Step 3. It writes a cover letter framed around what you would bring to the role. Step 4. A second AI agent reviews the first agent's work, points out weaknesses, and the first agent revises. Step 5. It compiles both into clean PDFs you can send. The whole thing is a folder of markdown files. The candidate profile, the writing style rules, the CV templates, the interview prep notes. Every step is plain text you can read and change. The job portal search is built for Danish boards. The application workflow itself works for any country. 489 stars. 270 forks. A fork-to-star ratio that high means people are using it, not only bookmarking. Mads is not a startup founder. He built this because he needed it for himself, then shared it. This is the future of job hunting. Not a service you pay for. A workflow you own. (Link in the comments)

Anthropic engineer: "You're not supposed to prompt Claude. You're supposed to build a system that prompts itself." this is one of the best workflows I've seen in a long time in this video she breaks down exactly how most people are using Claude: - the 14% you lose to CLAUDE.md before typing a word - the automation workflows most users don't know exist - the daily task pipelines that run without touching the keyboard - the daily workflows Anthropic's own engineers automated first if you've been using Claude for more than a month and never left the chat window, you've been using one agent when you could be running a team of them instead of another show tonight, watch this make sure to bookmark it before it gets lost in your feed the guide is in the article below

Khairallah AL-Awady @eng_khairallah1

a week ago

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34 79 487 1.1M 2K

I just had the craziest experience at the airport. We are about to board a flight to Atlanta when the pilot from the incoming plane walks out of the jetway. Guy is probably late 50s, salt and pepper hair, military look. The kind of pilot you instantly feel good about seeing on your flight. Pilot walks over to the counter, gets on the PA system, and starts addressing everyone. “Folks, I’ve been doing this a long time. Flying one of these jets is easy. The hard part is looking at 130 people and telling them their flight is going to be delayed.” Audible groans throughout the boarding gate. Most people here are flying to Atlanta as a layover before another flight. 130 people just had their day become a complete mess. The pilot goes on. “I get it, trust me. But here’s the deal: During our landing, we had a small mechanical issue. I’m not your pilot for the next leg, but I don’t feel confident the jet’s safe to fly until we have a mechanical team look it over, and I don’t feel comfortable asking the next pilots to fly you guys until we get confirmation.” He points at the agents next to him behind the counter: “Now, none of this is the agents’ fault. Please be kind to them. I’m the one who made this decision, not them, so any inconvenience you experience is my fault. Just please know that I don’t do this lightly, and I’m only doing it because I believe it’s in the best interests of everyone’s safety.” Now this is where the story gets crazy. The pilot puts the microphone down, grabs his suitcase, and all the people in the gate… Start clapping. I’m not joking, everyone starts clapping for the guy. 130 people who just had their travel plans ruined give an ovation to the guy who made the decision and delivered the message. All because he addressed them with decency and transparency, took ownership of the decision, made it clear that it was necessary, and explained why it was in everyone’s best interest. It’s honestly one of the best examples of strong communication—of strong leadership, for that matter—that I’ve seen in a long time. @Delta, whoever your Atlanta to Wichita pilot was this morning, he’s one of the good ones. Please tell him the delayed passengers of flight 1637 appreciate what he did.

J.P. Morgan pays $650,000+ a year for quants. They built this exact Python training to get you there & released it for free. Zero to quant directly from J.P. Morgan technologists. Bookmark this & read the article below before someone takes it down.

Roan @RohOnChain

a month ago

x.com/i/article/2048…

29 193 1K 3.5M 6K

Below is my recent presentation I did at @_groww and at @Bharat_Nivesh event. I talk about business and market cycles. And how these affects stocks. The cyclical ones as well as the non-cylical ones. Many case studies in the presentation. There is absolutely no investment advice or recommendation on any stocks mentioned in the presentation. Any stock in the presentation will only be there for the purpose of explaining investing concepts. Hope this is of help for value investors, primarily. slideshare.net/slideshow/buy-…

IIT Delhi to IIM Bangalore to IAS. I got the best education my country had to offer. It taught me how to crack tough exams and manage big responsibilities. But it never taught me how to quiet my own mind or handle loneliness. We spend many years learning how to achieve, but not a single day learning how to be happy. My thoughts on what is missing in school education. Emotional Regulation: We memorized the periodic table, but no one explained the chemistry of a broken heart. School demanded we stay quiet, confusing silence with peace. Now, we don't know how to host our own storms without drowning in them. We feel lost because we were taught to suppress, not to process. Deep Communication: We were taught to write perfect essays, but not how to say "I’m hurting" or "No." While there is a strong emphasis on communication, we are not taught the vocabulary of the adult life. There is no course on how to stand our ground in face of bullying by a boss or how to protect our work boundaries by saying 'No' Critical Thinking: In school, the person with the most answers won. In life, the person with the most questions survives. This is the reason many adults can repeat opinions confidently without ever questioning where those opinions came from. We are told everything as the gospel truth. So we end up just following blindly Financial Literacy: We spent years learning maths and solving for x, but never learned how to keep ourselves from falling in a debt trap. Money isn't just about math; it’s about the dignity of choice. We do not learn how to use debt effectively without it controlling our freedom. How impulsive spending compounds over time, or how money affects stress, relationships, and mental peace. Financial literacy is missing because education often focuses on earning money someday, not managing it wisely once it arrives. Self-Discipline School is a world of bells and schedules. Someone else always tells you what to do and when. But adulthood is a world of total silence. We feel stuck because we were never taught how to push ourselves without a teacher watching. Discipline is simply the habit of keeping promises to yourself. This is a habit many of us are lacking Handling Loneliness In school, you are always shrouded by people. You never realize how loud the silence of adulthood can be until you’re in it. We feel lonely because we weren't taught how to be our own best friends. Peace is learning that being alone doesn't mean being lonely. It is a sacred space, not a sign of being unwanted. Reading People School is a time of innocence where friendships are often given to you. But as we go along, not everyone retains that purity. We feel cheated because we weren't taught to see the hidden intentions or the masks people wear. Reading people is the quiet wisdom of seeing the truth behind the words. Mental Health Maintenance We have gym class for our bodies, but nothing for our souls. We are taught to push through exhaustion to finish a project, which is exactly how we end up in burnout. Honoring your nervous system is the only way to make sure the light inside you doesn't go out. We should know when we are dealing with a stressor and unable to handle it anymore. We should know when to reach out for help if we feel that we are drowning in that distress Knowing Yourself We spend years trying to be the "best" student, only to realize we don't know who we are without a gold medal. We are left inadequate because we studied every subject except our own souls. The ultimate education is discovering what truly matters to you before the world tells you what to want.

Krishna Rao is the CFO of Anthropic, and this is his first podcast appearance. He joined the company two years ago when run-rate revenue was about $250M. Today it is $30B. He has helped raise ~$75B and is responsible for the procurement and allocation of compute. I feel lucky we get to hear what it is like to sit inside a company this consequential at a moment this pivotal. We discuss: - The cone of uncertainty - How he allocates compute across Trainium, TPUs, and GPUs - What investors misunderstand about model companies - Why the returns to frontier intelligence keep rising - Platform vs application and where Anthropic builds its own products - How Anthropic uses Claude internally I have asked my closing question about the kindest thing more than 500 times. Krishna's answer is one I have never heard before. Enjoy! Timestamps: 0:00 Intro 2:38 The Compute Canvas 6:51 The "Cone of Uncertainty" 11:58 Why the Returns to Frontier Intelligence Are So High 16:45 Recursive Self-Improvement 20:20 Scaling Laws 23:30 Sourcing $100 Billion in Compute 28:05 Platform vs. Application Strategy 32:52 Pricing Dynamics 38:48 How Anthropic’s Finance Team Uses Claude 43:24 Raising Capital & Overcoming Investor Skepticism 52:32 Public Perception, Risks, and Government Regulation 57:25 Mythos Release 1:12:33 What Could Derail the AI Revolution? 1:13:47 Biotech and Healthcare 1:15:31 The Kindest Thing

The AI supercycle will last 15 years. We're in year 3. Most investors are still buying Phase 1 names while the real money is already rotating into Phase 3. I mapped the entire cycle into 4 phases with the tickers that matter at each stage: The AI supercycle is the biggest investment theme of our generation. Bigger than mobile. Bigger than cloud. A 15 year structural shift that will reshape every sector of the global economy. Hyperscalers just committed $725 billion in capex for 2026, nearly doubling last year. Microsoft, Google, Amazon, and Meta each spending over $100 billion individually. This is not speculation. I've mapped the entire supercycle into four phases so you know exactly where we are and where the asymmetric opportunities sit. 🔴 Phase 1: Already Ran (2023 to 2025) The foundation layer is complete. $AMD ran 78% in 2025, $NVDA 39%, and $INTC just posted a blowout Q1 that sent the Philadelphia Semiconductor Index above 10,000 for the first time. Chips still power every phase but the generational entries are gone and risk/reward has compressed. - $NVDA, $AMD, $ARM, $INTC, $AVGO, $MU, $GLW - Semiconductors, Memory & Storage,Photonics/Optics - Foundation complete. Still growing but priced for it. 🟠 Phase 2: Peak Buildout (2025 to 2027) The phase most investors just woke up to. $CEG acquired Calpine to become the largest U.S. private power producer at 55 GW. $GEV up over 200% in a year. $VRT co engineering cooling for NVIDIA's Rubin architecture. $GLW up 74% YTD on optical fiber demand. Nuclear SMRs are the breakout with $OKLO, $SMR, and $BWXT positioning to power data centers directly. Still upside but the obvious names have moved. - $CEG, $GEV, $VRT, $VST, $TLN, $ANET, $GLW, $MOD, $EQIX $OKLO, $SMR, $BWXT, $NNE - Power/Grid, Cooling, Networking, Nuclear/SMR Peak buildout. - Nuclear SMRs are the sleeper. 🟡 Phase 3: The Positioning Window (2026 to 2028) Where AI escapes the data center and enters the physical world. Most will be late. Tesla converting Fremont to Optimus production, $25B capex, mass production targeted H2 2026. Rocket Lab posted record $602M revenue with $1.85B backlog. $LUNR up 47% YTD with $943M in contracts. $KTOS Valkyrie drone selected for the Marine Corps. The window to position is open right now. - $TSLA, $RKLB, $LUNR, $KTOS, $AVAV, $PATH, $ISRG $MP, $FCX, $ALB, $ASTS - Robotics/Autonomy, Space/Defense/Drones, Rare Earths - This is where the asymmetric risk/reward lives. 🟢 Phase 4: Final Frontier (2028+) The endgame. Microsoft capex $190B. Alphabet $190B. Amazon $200B. Meta $145B. Google Cloud backlog past $460B. They're building the rails for AI software dominance and AGI. Quantum still early but $IONQ and D Wave are laying groundwork. The platforms that control the software layer win the entire supercycle. - $MSFT, $GOOGL, $AMZN, $META, $ORCL, $IONQ - AI Software Dominance, AGI Infrastructure Decade long thesis. - Accumulate on weakness. 💊 Key Takeaway - Phase 2 is confirmed ($725B hyperscaler capex) - Phase 3 is where the smart money positions nowRobotics, space, defense, nuclear - SMR are the 2026 to 2028 trades - Most will rotate into these names 12 months too late 15 year supercycle. Not a trade. Phase 1 ran. Phase 2 is priced. Phase 3 is where you want to be.

Anthropic just published a paper that should terrify every AI company on the planet. Including themselves. It is called subliminal learning. Published in Nature on April 15, 2026. Co-authored by researchers from Anthropic, UC Berkeley, Warsaw University of Technology, and the AI safety group Truthful AI. The finding: AI models inherit traits from other models through seemingly unrelated training data. GAI Audio Translation Archives Not through obvious contamination. Not through explicit labels. Through invisible statistical patterns embedded in outputs that look completely innocent — number sequences, code snippets, chain-of-thought reasoning — patterns no human reviewer would catch and no content filter would flag. Here is what the researchers actually did. They took a teacher AI model and fine-tuned it to have a specific hidden trait. A preference for owls. Then they had the teacher generate training data — number sequences, nothing else. No words. No context. No semantic reference to owls whatsoever. They rigorously filtered out every explicit reference to the trait before feeding the data to a student model. The student models consistently picked up that trait anyway. DataCamp The teacher had encoded invisible statistical fingerprints into its number outputs. Patterns so subtle that no human could detect them. Patterns that other AI models, specifically prompted to look for them, also failed to detect. The student absorbed them anyway. And became an owl-preferring model. Without ever seeing the word owl. That is the benign version of the experiment. Here is the dangerous one. The researchers ran the same experiment with misalignment — training the teacher model to exhibit harmful, deceptive behavior rather than an animal preference. The effect was consistent across different traits, including benign animal preferences and dangerous misalignment. OpenAIToolsHub The misalignment transferred. Invisibly. Through unrelated data. Into the student model. This means the following — and read this carefully. Every AI company in the world uses distillation. They take a large, capable teacher model. They generate synthetic training data from it. They use that data to train smaller, faster, cheaper student models. Every major deployment pipeline in enterprise AI runs on this technique. If the teacher model has any hidden bias, any subtle misalignment, any behavioral quirk baked into its weights — that trait can transmit silently into every student model trained on its outputs. Even if those outputs are filtered. Even if they look completely clean. Even if they contain zero semantic reference to the trait. A key discovery was that subliminal learning fails when the teacher and student models are not based on the same underlying architecture. A trait from a GPT-based teacher transfers to another GPT-based student but not to a Claude-based student. Different architectures break the channel. OpenAIToolsHub Which means the transmission is architecture-specific. Which means it operates below the level of content. Which means content filtering — the primary defense the entire industry relies on — does not stop it. The researchers' own words: "We don't know exactly how it works. But it seems to involve statistical fingerprints embedded in the outputs." GAI Audio Translation Archives Anthropic published this paper about their own technology. The company that built Claude looked at how AI models train each other and found an invisible transmission channel for harmful behavior that nobody knew existed. They published it anyway. Because the alternative — knowing it and saying nothing — is worse. Source: Cloud, Evans et al. · Anthropic + UC Berkeley + Truthful AI · Nature · April 15, 2026 · arxiv.org/abs/2507.11408

Jane Street pays $650,000 a year for quants. MIT wrote the exact bible to get there & released it for free. 51 pages. Zero to quant. Probability, stats, market making, real interview questions from Jane Street, Citadel, Two Sigma & more. Bookmark, before someone takes it down.

Roan @RohOnChain

a month ago

x.com/i/article/2048…

29 193 1K 3.5M 6K

🚨BREAKING: Claude can now write your entire job application like a top recruiter. Here are 10 prompts that turn a job description into a tailored CV, cover letter, and interview prep guide in under 5 minutes. (Save this)

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What do the smartest kids in the world do when they grow up? I did the largest study of ~18,000 International Olympiad medalists (IMO, IOI and IPhO) over the last 25yrs, arguably the sharpest analytical minds of the world in high school, to see where they ended up and traced ~50% of them. Founders of ~20 unicorns and ~7 decacorns and ~10 billionaires: OpenAI, Cursor, Stripe, Databricks, Perplexity, Ethereum, Cognition, Hyperliquid, Fireworks, Modal, Quora, Parallel, Cartesia, Wispr Most kids went to MIT, a whopping 12% of them, followed by Cambridge (7%) and Sharif (3%)! The career paths they chose (of those who graduated) were: — 36% Academia (professors) — 26% Other — 22% in Software / Tech — 12% in Quant / Finance — 5% Founders! The biggest employer was Google, by far, at 6%. Others interesting tidbits were: — 47 of them work at Jane Street (#3) — 38 at OpenAI (#5) — 15 at Anthropic — 8 at Cognition — 6 at Isomorphic Labs Olympiaders were 1500x more likely to be billionaires and 4000x more likely to be unicorn founders than the average person!

i come back to this clip of Jim Simons every few months. the man who built the most successful hedge fund in history sharing his guiding principles in life. 2 and a half minutes that will stick with you.

Her name is Malavika Hegde. In July 2019, her husband VG Siddhartha walked onto a bridge near Mangalore and told his driver he would be back shortly. He never came back. Siddhartha had built Cafe Coffee Day into India’s largest coffee chain. 1700 cafes. 25000 employees. He had also buried the company under Rs 7214 crore of debt. The board panicked. Analysts wrote CCD off. Nobody had accounted for Malavika. She was an engineer. A mother of two. She had never run a company in her life. Her father SM Krishna was a former Chief Minister of Karnataka. She could have lived in that shadow comfortably. She chose the debt instead. In December 2020, she took over as CEO. In the middle of a pandemic. Her first act was a personal letter to all 25000 employees. She wrote the CCD story is worth preserving. I will fight for this brand. Then she got to work. She called every lender personally and renegotiated terms. She sold Global Village Tech Park to Blackstone. She closed every loss making outlet. She exported premium Arabica beans from her own 20000 acre farm to international markets to generate fresh revenue. She never raised the price of a single cup of coffee. By 2021, the debt was down to Rs 1731 crore. By 2023, it was Rs 465 crore. A 93 percent reduction in four years. Today, CCD runs 423 cafes and 55733 vending machines across India. Netflix is developing a web series on her life. Business schools are teaching her turnaround as a case study. She had no playbook. She had no mentor. She had grief and 25000 people depending on her. That was enough. Follow for real stories India never makes headlines about.

Worth a read! 😍 My mom wanted to send me homemade pickles. But I said ‘no’. I was 27, living in New York, working on Wall Street. I didn't need pickles shipped across the world. The shipping would cost more than buying them here. Three years later, I read the psychologist take on what I'd actually done. When you reject someone's offer to help, you're not just declining assistance. You're declining their need to matter to you! Benjamin Franklin figured this out in 1736. He had a rival in the Pennsylvania legislature who hated him. Instead of trying to win him over with favors, Franklin asked the rival to lend him a rare book. The rival agreed. They became lifelong friends. It's called the Ben Franklin effect.When people do something for you, they convince themselves they must like you. Otherwise, why would they help? My mom didn't want to send pickles because I needed them. She wanted to send them because SHE needed to feel useful to me. To feel like despite the ocean between us, she still had a role in my life. Every time I said "I'll manage," I was taking that away from her. Here's what I learned after a decade of living away from home: → Accepting small favors isn't about you needing help. It's about letting people you love feel needed. Your dad wants to transfer ₹5000 even though you earn well? Let him. Your friend wants to pick you up from the airport even though Uber exists? Say yes. Your partner wants to make you tea even though you can make it yourself? Accept it. The people who love you don't want to solve your big problems. They want to matter in your small moments. Let them. #lifelesson

🚨 GOOGLE, META, OPENAI etc. BIG TECH are REJECTING JOB CANDIDATES BEFORE EVEN THEY FINISH TALKING. 50 LLM QUESTIONS. IF YOU CAN'T ANSWER THEM, THE INTERVIEW ENDS BEFORE IT STARTS. The people passing these interviews are walking out with $200k+ offers. Someone just LEAKED THE EXACT LLM INTERVIEW QUESTIONS these companies are asking right now. And the gap between people who know these answers and people who do not is already costing careers. Here is every category you need to know: The Basics they always ask first: ↳ How does tokenization work and why does it matter ↳ How does attention actually work inside a transformer ↳ What is a context window and what breaks when it gets too big ↳ What are embeddings and how do they get initialized ↳ How does the model know word order without reading left to right The fine-tuning questions that eliminate 80% of candidates: ↳ What is LoRA and why is it better than full fine-tuning ↳ What is QLoRA and when do you use it instead ↳ How do you fine-tune a model without making it forget everything it already knows ↳ What is model distillation and why do companies use it ↳ How do you handle vocabularies with millions of possible words The generation questions most people guess on: ↳ Beam search vs greedy decoding, which one and when ↳ What temperature actually does to model output ↳ The difference between top-k and top-p sampling ↳ Why autoregressive models work differently from masked models The advanced concepts that separate good from great: ↳ How RAG works and why it beats fine-tuning for factual accuracy ↳ Why Chain-of-Thought prompting makes models dramatically smarter ↳ What Mixture of Experts is and why every frontier model uses it now ↳ Zero-shot vs few-shot learning and when each one wins The math questions that make people sweat: ↳ Why softmax is used inside attention and not something simpler ↳ What cross-entropy loss actually measures ↳ What KL divergence is and where it shows up in AI training ↳ Why vanishing gradients were destroying transformers and how they fixed it If you are applying for any AI role in 2026 and you cannot answer at least 40 of these, you are not ready yet. The full list of 50 questions is worth printing out and going through one by one. Save this post. Your next interviewer has almost certainly pulled from this exact list.